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Open AccessArticle

Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field

by Fan Hu 1, Wen Yang 1,*, Jiayu Chen 1 and Hong Sun 1,2
1
Signal Processing Laboratory, School of Electronic Information, Wuhan University, Wuhan 430072, China
2
TSI Department, TELECOM ParisTech, F-75013 Paris, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(5), 2275-2291; https://doi.org/10.3390/rs5052275
Received: 1 March 2013 / Revised: 3 May 2013 / Accepted: 7 May 2013 / Published: 13 May 2013
This paper proposes a multi-level max-margin discriminative analysis (M3DA) framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images. In order to generate more discriminative topic-level features, the M3DA uses the maximum entropy discrimination latent Dirichlet Allocation (MedLDA) model. Moreover, for improving the spatial coherence of visual words neglected by M3DA, conditional random field (CRF) is employed to optimize the soft label field composed of multiple label posteriors. The framework of M3DA enables one to combine word-level features (generated by support vector machines) and topic-level features (generated by MedLDA) via the bag-of-words representation. The experimental results on high-resolution satellite images have demonstrated that, using the proposed method can not only obtain suitable semantic interpretation, but also improve the annotation performance by taking into account the multi-level semantics and the contextual information. View Full-Text
Keywords: satellite images annotation; topic model; MedLDA; multi-level max-margin; conditional random field satellite images annotation; topic model; MedLDA; multi-level max-margin; conditional random field
MDPI and ACS Style

Hu, F.; Yang, W.; Chen, J.; Sun, H. Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field. Remote Sens. 2013, 5, 2275-2291.

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